
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
__lowerCAmelCase     :	Tuple       			=  logging.getLogger(__name__)

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			argparse.ArgumentParser(
      description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""   )
  parser.add_argument("""--file_path""",	type=A_,	default="""data/dump.txt""",	help="""The path to the data."""   )
  parser.add_argument("""--tokenizer_type""",	type=A_,	default="""bert""",	choices=["""bert""", """roberta""", """gpt2"""]   )
  parser.add_argument("""--tokenizer_name""",	type=A_,	default="""bert-base-uncased""",	help="""The tokenizer to use."""   )
  parser.add_argument("""--dump_file""",	type=A_,	default="""data/dump""",	help="""The dump file prefix."""   )
  __magic_name__								=			parser.parse_args()

  logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''   )
  if args.tokenizer_type == "bert":
    __magic_name__								=			BertTokenizer.from_pretrained(args.tokenizer_name   )
    __magic_name__								=			tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
    __magic_name__								=			tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
  elif args.tokenizer_type == "roberta":
    __magic_name__								=			RobertaTokenizer.from_pretrained(args.tokenizer_name   )
    __magic_name__								=			tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
    __magic_name__								=			tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
  elif args.tokenizer_type == "gpt2":
    __magic_name__								=			GPTaTokenizer.from_pretrained(args.tokenizer_name   )
    __magic_name__								=			tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
    __magic_name__								=			tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

  logger.info(f'''Loading text from {args.file_path}'''   )
  with open(args.file_path,	"""r""",	encoding="""utf8"""   ) as fp:
    __magic_name__								=			fp.readlines()

  logger.info("""Start encoding"""   )
  logger.info(f'''{len(A_   )} examples to process.'''   )

  __magic_name__								=			[]
  __magic_name__								=			0
  __magic_name__								=			10000
  __magic_name__								=			time.time()
  for text in data:
    __magic_name__								=			f'''{bos} {text.strip()} {sep}'''
    __magic_name__								=			tokenizer.encode(A_,	add_special_tokens=A_   )
    rslt.append(A_   )

    iter += 1
    if iter % interval == 0:
      __magic_name__								=			time.time()
      logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''   )
      __magic_name__								=			time.time()
  logger.info("""Finished binarization"""   )
  logger.info(f'''{len(A_   )} examples processed.'''   )

  __magic_name__								=			f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
  __magic_name__								=			tokenizer.vocab_size
  if vocab_size < (1 << 16):
    __magic_name__								=			[np.uintaa(A_   ) for d in rslt]
  else:
    __magic_name__								=			[np.intaa(A_   ) for d in rslt]
  random.shuffle(rslt_   )
  logger.info(f'''Dump to {dp_file}'''   )
  with open(A_,	"""wb"""   ) as handle:
    pickle.dump(rslt_,	A_,	protocol=pickle.HIGHEST_PROTOCOL   )


if __name__ == "__main__":
    main()



import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					int     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      None:







    """simple docstring"""






    warnings.warn(
        """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
        """ use BeitImageProcessor instead."""     ,				UpperCamelCase__     ,				)
    super().__init__(*UpperCamelCase__     ,				**UpperCamelCase__       )

